The Advanced Land Imager (ALI) was developed at Lincoln Laboratory under the sponsorship of the National Aeronautics and Space Administration (NASA). The purpose of ALI was to validate in space new technologies that could be utilized in future Landsat satellites, resulting in significant economies of mass, size, power consumption, and cost, and in improved instrument sensitivity and image resolution. The sensor performance on orbit was verified through the collection of high-quality imagery of the earth as seen from space. ALI was launched onboard the Earth Observing 1 (EO-1) satellite in November of 2000 and inserted into a 705 km circular, sun-synchronous orbit, flying in formation with Landsat 7. Since then, ALI has met all its performance objectives and continues to provide good science data long after completing its original mission duration of one year. This article serves as a brief introduction to ALI and to six companion articles on ALI in this issue of the Journal.

The Earth Observing 1 (EO-1) satellite, developed under NASA’s New Millennium Program, was successfully launched on 21 November 2000 from Vandenberg Air Force Base, California. The primary land-sensing instrument for this mission is the Advanced Land Imager (ALI), designed and developed by Lincoln Laboratory. ALI collected its first image just five days after launch. Although EO-1 was intended to be a one-year technology-validation mission, ALI still continues to produce valuable science data after five years in orbit. Lincoln Laboratory personnel have evaluated in detail the technical performance of ALI. A team of experienced earth scientists assembled by NASA has also assessed the quality of the science products obtained from ALI and compared the results directly with those obtained from the Enhanced Thematic Mapper Plus sensor on Landsat 7. The results demonstrate that ALI has superior performance in resolution, sensitivity, and dynamic range. These investigations have provided critical input for the design and implementation of next-generation Landsat imagers. This article summarizes the initial ALI program development and Lincoln Laboratory’s role in the mission formulation. We also give an overview of ALI instrument design and performance. Finally, we discuss the relevance of ALI to future land remote sensing instruments.

The Advanced Land Imager (ALI), currently in operation on the Earth Observing 1 (EO-1) satellite, was developed at Lincoln Laboratory during the period from mid 1996 through early 1999. Within the significant constraints of size, mass, power consumption, and cost, the ALI was designed to improve on the current Landsat land-sensing satellite technology, providing spectral and radiometric data with high resolution and stability. This article describes the mechanical design, integration, and testing of the ALI at Lincoln Laboratory prior to the launch of the EO-1 in November 2000.

The calibration and performance assessment of the Advanced Land Imager (ALI) on the Earth Observing 1 (EO-1) satellite required a ground data system for acquiring and processing ALI data. In order to meet tight schedule and budget requirements, we developed an automated system that could be run by a single operator. This article describes the overall ground data system and the individual electrical ground support equipment (EGSE) and computer components used. The ALI calibration control node (ACCN) serves as a test executive with a single graphical user interface to the system, controlling calibration equipment and issuing data acquisition and processing requests to the EGSE and other computers. EGSE1, a custom data acquisition system, collects ALI science data and passes ALI commanding and housekeeping telemetry collection requests to EGSE2 and EGSE3, which are implemented on a custom integration and test workstation. A performance assessment machine stores and processes collected ALI data, and automatically displays quick-look processing results. We also describe the custom communications protocol developed to interface these machines and to automate their interactions, including the various modes of operation needed to support spatial, radiometric, spectral, and functional calibration and performance assessment of the ALI.

The Advanced Land Imager (ALI) is currently flying aboard the Earth Observing 1 (EO-1) mission under NASA’s New Millennium Program. Initial ground calibration and on-orbit calibration procedures for the ALI were established to ensure accurate spectral and radiometric measurements. Periodic recertification is performed with solar, lunar, and onboard lamp sources. As a result of the on-orbit data stability and drift, several mechanical steps, including temperature cycling, have been implemented.

In 1998, the imaging properties of the Advanced Land Imager on the Earth Observing 1 (EO-1) satellite were calibrated at Lincoln Laboratory under simulated flight conditions. The spatial calibrations estimated the system transfer functions for the various spectral bands, and the line of sight of each detector, relative to a reference cube on the instrument. The Advanced Land Imager is a push-broom scanner. For each spectral band, a two-dimensional image is acquired one line at a time as the spacecraft moves over the scene. To reconstruct a normal image, we take into account the staggered placement of the detectors on the focal plane, the distortions in the optical system, and the speed and orientation of the spacecraft. Applying the line-of-sight calibration data, we have produced system-corrected images from cues in the image data alone. We create browse images to show the entire scanned scene in any three of the available multispectral bands or in the panchromatic band. The panchromatic data have also been combined with the red, green, and blue multispectral data to produce natural-color images with ten-meter ground-sampling distance. Following the launch of EO-1 in November 2000, the imaging performance was assessed from the on-orbit data. This was primarily done through analysis of images of bridges. In addition, the moon, stars, and planets were scanned. To reconstruct celestial images requires reference to the position and attitude data from the telemetry stream. The moon in particular provided valuable data to assess weak stray-light and ghost-image artifacts. In general, the images returned from orbit confirm the pre-launch calibrations, and fully satisfy the requirements developed for them at the start of the program.

In the course of processing the image data from the Advanced Land Imager (ALI), we routinely form browse images, in addition to producing the radiometrically and geometrically corrected scientific data files. The browse images are intended for easy viewing, to show quickly what each scene contains, and to determine whether the objects of interest are obscured by clouds. These images are written out as JPEG files, which are readily opened, viewed, cropped, sharpened, and color corrected in desktop applications such as Adobe Photoshop. ALI bands 3, 2, and 1 sense the visible red, green, and blue wavelengths. Therefore, when we select those bands to form a browse image, and we map bands 3, 2, and 1 into the RGB color space, we obtain a natural-color image. For brevity, we usually refer to this as a 3-2-1 image. We do not claim that the result, especially when printed, is a colorimetrically exact reproduction of the scene. In fact, the color levels have been adjusted to remove most of the path radiance leaving the top of the atmosphere. The aim is generally to produce the colors that we would expect to see from a low-flying airplane. Because the ALI produces nine multispectral bands, many other combinations of color mapping are possible in the browse images. Frequently, bands 4, 3, and 2 are used for the RGB inputs. This combination is chosen because healthy vegetation is highly reflective in near-infrared band 4, and shows up as bright red in the 4-3-2 images. This gallery contains examples of such images, as well as images produced from other band combinations. In addition, we sometimes combine the data from bands 3, 2, and 1, which are sampled at 30 m resolution, with the panchromatic band data, which is sampled at 10 m, to produce a color image with 10 m resolution.

The Earth Observing 1 (EO-1) satellite has three imaging sensors: the multispectral Advanced Land Imager (ALI), the hyperspectral Hyperion sensor, and the Atmospheric Corrector. Hyperion is a high-resolution hyperspectral imager capable of resolving 220 spectral bands (from 0.4 to 2.5 μm) with a 30 m resolution. The instrument images a 7.5 km by 100 km surface area. Since the launch of EO-1 in late 2000, Hyperion is the only source of spaceborne hyperspectral imaging data. To demonstrate the utility of the EO-1 sensor data, this article gives three examples of EO-1 data applications. A cloud-cover detection algorithm, developed for processing the Hyperion hyperspectral data, uses six bands in the reflected solar spectral regions to discriminate clouds from other bright surface features such as snow, ice, and desert sand. The detection technique was developed by using twenty Hyperion test scenes with varying cloud amounts, cloud types, underlying surface characteristics, and seasonal conditions. When compared to subjective estimates, the algorithm was typically within a few percent of the estimated total cloud cover. The unique feature-extraction capability of hyperspectral sensing is also well suited to coastal characterization, which is a more complex task than deep ocean characterization. To demonstrate the potential value of Hyperion data (and hyperspectral imaging in general) to coastal characterization, EO-1 data from Chesapeake Bay are analyzed. The results compare favorably with data from other satellite and aircraft data sources. Finally, to demonstrate additional utility of EO-1 data, we describe how a combined analysis of panchromatic, multispectral, and hyperspectral data can be applied to terrain characterization and anomaly detection.

Estimation techniques based on neural networks are becoming more common in high-resolution atmospheric remote sensing largely because of the simplicity, flexibility, and ability of the neural network techniques to accurately represent complex multidimensional statistical relationships. Spaceborne atmospheric sounders with increasingly finer spatial and spectral resolution are generating formidable amounts of radiance data. This abundance of data presents two major challenges in the development of algorithms that retrieve geophysical information from the radiance measurements. The first challenge concerns the robustness of the retrieval operator and involves maximal use of the geophysical content of the radiance data with minimal interference from instrument and atmospheric noise. The second challenge is the implementation of the robust algorithm within a given computational budget. The neural network estimation techniques described in this article allow both of these challenges to be overcome. Sample results are presented for retrievals of (1) atmospheric temperature and moisture profiles and (2) precipitation rates.

The polarization of electromagnetic signals is an important feature in the design of modern radar and telecommunications. From standard electromagnetic theory it is readily shown that a linearly polarized plane wave propagating in free space comprises two equal but counter-rotating components of circular polarization. In magnetized media, these circular modes can be arranged to produce the nonreciprocal propagation effects that are the basic properties of isolator and circulator devices. Independent phase control of right-hand (+) and left-hand (–) circular waves is accomplished by a splitting of their propagation velocities through differences in the ε±μ± parameter. A phenomenological analysis of the permeability μ and permittivity ε in dispersive media serves to introduce the corresponding magnetic and electric dipole mechanisms of interaction with the propagating signal. As an example of permeability dispersion, a Lincoln Laboratory quasi-optical Faraday rotation isolator/circulator at 35 GHz (wavelength λ ~ 1 cm) with a garnet ferrite rotator element is described. At infrared (IR) wavelengths (λ = 1.55 μm), where fiber optic laser sources also require protection by passive isolation of the Faraday rotation principle, ε rather than μ provides the dispersion and the frequency is limited to the quantum energies of the electric dipole atomic transitions peculiar to the molecular structure of the magnetic garnet. For optimum performance, bismuth additions to the garnet chemical formula are usually necessary. Spectroscopic and molecular theory models developed at Lincoln Laboratory to explain the bismuth effects are reviewed. In a concluding section, proposed advances in present technology are discussed in the context of future radar and telecommunications challenges.

Research in human language technology has made great progress in the past few years. Automatic speech recognition systems are currently capable of producing English text transcripts of conversational telephone speech at a word-error rate of 15.2%, a reduction of 53% over the past five years. Arabic-to-English machine translation systems are capable of producing English text output at a word-error rate of 47% for a weighted word sequence recognition measure, an increase of over 300% over the past three years. These measures of performance are the result of technology-centered evaluations. In a collaborative, interdisciplinary project involving Lincoln Laboratory, the MIT Department of Brain and Cognitive Sciences, and the Defense Language Institute Foreign Language Center, we have been addressing the question of how these remarkable gains in automatic speech recognition are reflected in measures of effectiveness, which show the impact of the recognition technology on the effectiveness of human users in accomplishing real-world language translation tasks. For both machine translation and speech recognition, we have developed techniques to scientifically measure the effectiveness of these technologies when they are utilized by human subjects.